- DAA - Discussion
- DAA - Useful Resources
- DAA - Quick Guide
- DAA - Hill Climbing Algorithm
- NP Hard & NP-Complete Classes
- DAA - Cook’s Theorem
- DAA - P and NP Class
- DAA - Vertex Cover
- DAA - Max Cliques
- Deterministic vs. Nondeterministic Computations
- DAA - Sublist Search
- DAA - Fibonacci Search
- DAA - Exponential Search
- DAA - Jump Search
- DAA - Interpolation Search
- DAA - Binary Search
- DAA - Linear Search
- Searching Techniques Introduction
- DAA - Radix Sort
- DAA - Counting Sort
- DAA - Bucket Sort
- DAA - Heap Sort
- DAA - Shell Sort
- DAA - Selection Sort
- DAA - Insertion Sort
- DAA - Bubble Sort
- DAA - Extract Method
- DAA - Heapify Method
- DAA - Insert Method
- DAA - Binary Heap
- Optimal Cost Binary Search Trees
- DAA - Multistage Graph
- DAA - Shortest Paths
- DAA - Spanning Tree
- Travelling Salesperson Approximation Algorithm
- Set Cover Problem
- Vertex Cover Problem
- Approximation Algorithms
- Fisher-Yates Shuffle
- Karger’s Minimum Cut
- Randomized Quick Sort
- Randomized Algorithms
- Travelling Salesman Problem | Dynamic Programming
- Longest Common Subsequence
- DAA - 0-1 Knapsack
- Floyd Warshall Algorithm
- Matrix Chain Multiplication
- DAA - Dynamic Programming
- DAA - Optimal Merge Pattern
- DAA - Job Sequencing with Deadline
- DAA - Fractional Knapsack
- Map Colouring Algorithm
- Dijkstra’s Shortest Path Algorithm
- Kruskal’s Minimal Spanning Tree
- Travelling Salesman Problem
- DAA - Greedy Method
- Towers of Hanoi
- Karatsuba Algorithm
- Strassen’s Matrix Multiplication
- DAA - Binary Search
- DAA - Merge Sort
- DAA - Max-Min Problem
- DAA - Divide & Conquer
- DAA - Space Complexities
- Master’s Theorem
- Time Complexity
- Asymptotic Notations & Apriori Analysis
- DAA - Methodology of Analysis
- DAA - Analysis of Algorithms
- DAA - Introduction
- Home
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Design and Analysis Vertex Cover
A vertex-cover of an undirected graph G = (V, E) is a subset of vertices V ⊆ V such that if edge (u, v) is an edge of G, then either u in V or v in V or both.
Find a vertex-cover of maximum size in a given undirected graph. This optimal vertexcover is the optimization version of an NP-complete problem. However, it is not too hard to find a vertex-cover that is near optimal.
APPROX-VERTEX_COVER (G: Graph) c ← { } E ← E[G] while E is not empty do Let (u, v) be an arbitrary edge of E c ← c U {u, v} Remove from E every edge incident on either u or v return c
Example
The set of edges of the given graph is −
{(1,6),(1,2),(1,4),(2,3),(2,4),(6,7),(4,7),(7,8),(3,8),(3,5),(8,5)}

Now, we start by selecting an arbitrary edge (1,6). We epminate all the edges, which are either incident to vertex 1 or 6 and we add edge (1,6) to cover.

In the next step, we have chosen another edge (2,3) at random

Now we select another edge (4,7).

We select another edge (8,5).

Hence, the vertex cover of this graph is {1,2,4,5}.
Analysis
It is easy to see that the running time of this algorithm is O(V + E), using adjacency pst to represent E .
Advertisements